Dynamic generalized linear models for monitoring endemic diseases: moving beyond univariate process monitoring control algorithms

TY - CONF

T1 - Dynamic generalized linear models for monitoring endemic diseases

T2 - Annual Meeting of the Society for Veterinary Epidemiology and Preventive Medicine - 2016

AU - Lopes Antunes, Ana Carolina

AU - Jensen, Dan Børge

AU - Halasa, Tariq

AU - Toft, Nina

PY - 2016/3

Y1 - 2016/3

N2 - The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control and eradication programmes based on changes in PRRS sero-prevalence was explored. Results showed a declining trend in PRRS sero-prevalence between 2007 and 2014 suggesting that Danish herds are slowly eradicating PRRS. The simulation study demonstrated the flexibility of DGLMs in adapting to changes intrends in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance of endemic and (re-)emerging diseases.

AB - The objective was to use a Dynamic Generalized Linear Model (DGLM) based on abinomial distribution with a linear trend, for monitoring the PRRS (Porcine Reproductive and Respiratory Syndrome sero-prevalence in Danish swine herds. The DGLM was described and its performance for monitoring control and eradication programmes based on changes in PRRS sero-prevalence was explored. Results showed a declining trend in PRRS sero-prevalence between 2007 and 2014 suggesting that Danish herds are slowly eradicating PRRS. The simulation study demonstrated the flexibility of DGLMs in adapting to changes intrends in sero-prevalence. Based on this, it was possible to detect variations in the growth model component. This study is a proof-of-concept, demonstrating the use of DGLMs for monitoring endemic diseases. In addition, the principles stated might be useful in general research on monitoring and surveillance of endemic and (re-)emerging diseases.

UR - https://curis.ku.dk/admin/files/174127172/SVEPM_Proceedings_Antunes.pdf

M3 - Paper

Y2 - 16 March 2016 through 18 March 2017

ER -